Autoregressive Energy Machines
Authors: Charlie Nash, Conor Durkan
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The Autoregressive Energy Machine achieves state-of-the-art performance on a suite of density-estimation tasks. and 4. Experiments For our experiments, we use a Res MADE with four residual blocks for the ARNN, as well as a fully-connected residual architecture for the ENN, also with four residual blocks. |
| Researcher Affiliation | Academia | 1School of Informatics, University of Edinburgh, United Kingdom. Correspondence to: Charlie Nash <charlie.nash@ed.ac.uk>, Conor Durkan <conor.durkan@ed.ac.uk>. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | code is available at https://github.com/conormdurkan/ autoregressive-energy-machines. |
| Open Datasets | Yes | UCI machine learning repository (Dheeru & Karra Taniskidou, 2017), and BSDS300 datasets of natural images (Martin et al., 2001). |
| Dataset Splits | Yes | Then, we compute the integral of the log unnormalized density corresponding to that onedimensional conditional, using a log-trapezoidal rule and context vectors generated from a held out-validation set of 1000 samples. and The KDE bandwidth and proposal distribution mixture weighting are optimized on the validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the main text of the paper. |
| Software Dependencies | No | The paper mentions using "Adam optimizer" but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or other library versions). |
| Experiment Setup | Yes | For our experiments, we use a Res MADE with four residual blocks for the ARNN, as well as a fully-connected residual architecture for the ENN, also with four residual blocks. The number of hidden units in the Res MADE is varied per task. We use the Adam optimizer (Kingma & Ba, 2014), and anneal the learning rate to zero over the course of training using a cosine schedule (Loshchilov & Hutter, 2016). For some tasks, we find regularization by dropout (Srivastava et al., 2014) to be beneficial. |